detecting-business-email-compromise-with-ai
by mukul975Detect business email compromise with AI using NLP, stylometry, behavioral signals, and relationship context. This detecting-business-email-compromise-with-ai skill helps SOC, fraud, and Security Audit teams score suspicious emails, explain risk signals, and decide whether to quarantine, warn, or escalate.
This skill scores 71/100, which means it is acceptable to list and should be useful for users who want a BEC-detection workflow rather than a generic cybersecurity prompt. The repository gives enough concrete structure—scripts, workflows, references, and thresholded actions—for agents to understand the task and act with less guesswork, but users should still expect some implementation/operational gaps before adopting it in production.
- Provides a concrete BEC detection workflow with feature extraction, multi-model analysis, confidence scoring, and defined action thresholds.
- Includes runnable support artifacts: two Python scripts plus workflow, standards, API reference, and template files that improve agent leverage.
- Frontmatter is valid and well-scoped to cybersecurity/phishing defense with relevant tags, techniques, and domain metadata.
- No install command or explicit quick-start instructions in SKILL.md, so users may need to infer how to activate and run it.
- The repository leans on accuracy/performance claims and structured detection concepts, but the visible excerpt does not show full end-to-end operational guidance or validation data.
Overview of detecting-business-email-compromise-with-ai skill
What this skill does
The detecting-business-email-compromise-with-ai skill helps you detect BEC-style emails by combining NLP, stylometry, behavioral signals, and relationship context instead of relying only on rules or blocklists. It is built for the detecting-business-email-compromise-with-ai skill use case where the message looks legitimate but the request is suspicious.
Who should use it
Use this detecting-business-email-compromise-with-ai skill if you work on SOC triage, email security tuning, fraud response, or a detecting-business-email-compromise-with-ai for Security Audit. It is most useful when you need a practical way to score messages, explain why they look risky, and decide whether to quarantine, warn, or escalate.
Why it is different
The repo is not just a generic phishing prompt. It includes a detection workflow, threshold guidance, feature ideas, and scripts that reflect real BEC signals such as urgency, secrecy, payment requests, sender impersonation, and changes from historical writing style. That makes it better suited to operational review than a one-off prompt.
How to Use detecting-business-email-compromise-with-ai skill
Install and locate the workflow files
For detecting-business-email-compromise-with-ai install, add the skill with npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai. Then read SKILL.md first, followed by references/workflows.md, references/api-reference.md, references/standards.md, and assets/template.md to understand the expected detection flow and scoring outputs.
Turn a rough goal into a good prompt
The skill works best when you supply a concrete detection task, not just “analyze this email.” A stronger detecting-business-email-compromise-with-ai usage prompt includes the message text, the sender role, known business context, and the action you want. Example: “Classify this email for BEC risk, compare it to the CFO’s usual tone, note impersonation or payment-request indicators, and recommend quarantine, warning, or deliver.”
What input matters most
Give the model enough context to judge authenticity: sender name and domain, reply chain, requested action, urgency language, payment details, and any baseline writing samples if you have them. For detecting-business-email-compromise-with-ai guide quality, the biggest lift comes from historical examples of legitimate mail and from policy thresholds that define what should trigger escalation.
Suggested working process
Start with a single email and ask for a scored verdict plus indicators, then test against a small batch of known benign and known malicious messages. Use the output to tune thresholds, false-positive tolerance, and reviewer actions. If you are using the scripts, treat them as a reference implementation for feature extraction and scoring, not as a complete production pipeline.
detecting-business-email-compromise-with-ai skill FAQ
Is this better than a normal prompt?
Yes, if you need repeatable BEC triage. A normal prompt can summarize suspicious language, but the detecting-business-email-compromise-with-ai skill is more useful when you want a structured result: risk score, rationale, behavioral mismatch, and action recommendation.
Does it require ML expertise to use?
No. Beginner users can still use the detecting-business-email-compromise-with-ai skill by providing the email and a short description of the expected sender behavior. ML familiarity helps when you want to adjust thresholds, baselines, or feature weights, but it is not required to get value from the workflow.
When should I not use it?
Do not use it for simple spam filtering, mass marketing cleanup, or situations where you only need a regex rule. It is also a poor fit when you have no business context at all, because BEC detection depends on intent, authority, and behavioral deviation.
How does it fit with security operations?
It fits best as an analyst assist layer in SOC workflows, email gateway tuning, or fraud review queues. For detecting-business-email-compromise-with-ai for Security Audit, use it to document why a message was flagged, which signals were present, and whether the control should auto-quarantine or only warn.
How to Improve detecting-business-email-compromise-with-ai skill
Give stronger baselines and roles
The biggest quality gain comes from sender-specific baselines. Include prior legitimate messages, the sender’s normal tone, title, common recipients, and usual request types so the detecting-business-email-compromise-with-ai skill can compare style and intent instead of guessing from one email alone.
State the decision policy up front
Tell the skill what action should follow each risk band: alert, warn, quarantine, or escalate. If you want useful operational output, specify the cost of false positives versus false negatives. That keeps the detecting-business-email-compromise-with-ai guide aligned with your environment instead of returning generic risk language.
Watch for common failure modes
The main failure mode is overreacting to urgency words without confirming impersonation or request anomaly. Another is missing BEC when the email is polite, short, and link-free. Improve the detecting-business-email-compromise-with-ai usage by asking for both positive indicators and reasons the message may still be legitimate.
Iterate with labeled examples
After the first pass, feed back a few labeled messages: true BEC, false positive, and missed BEC. Use those examples to refine prompts, adjust thresholds, and update feature emphasis. The more you close the loop, the better the skill performs for a real detecting-business-email-compromise-with-ai install in Security Audit or SOC review.